Combining structural performance and designer preferences in evolutionary design space exploration

• Many high-performing structural solutions exist that vary in aesthetic quality.
• Standard optimization is limited by its single result and quantitative-only nature.
• Interactive evolutionary algorithms combine quantitative and qualitative goals.
• Mutation rate and generation size offer opportunities to drive design exploration.
• Users can decide how to balance cost, aesthetics, and diversity of design options.

This paper addresses the need to consider both quantitative performance goals and qualitative requirements in conceptual design. A new computational approach for design space exploration is proposed that extends existing interactive evolutionary algorithms for increased inclusion of designer preferences, overcoming the weaknesses of traditional optimization that have limited its use in practice. This approach allows designers to set the evolutionary parameters of mutation rate and generation size, in addition to parent selection, in order to steer design space exploration. This paper demonstrates the potential of this approach through a numerical parametric study, a software implementation, and series of case studies.